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Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking
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Recent advances in large language models (LLMs), such as OpenAI-o1 and DeepSeek-R1, have demonstrated the effectiveness of test-time scaling, where extended reasoning processes substantially enhance model performance. Despite this, current models are constrained by limitations in handling long texts and reinforcement learning (RL) training efficiency. To address these issues, we propose a simple yet effective test-time scaling approach Multi-round Thinking. This method iteratively refines model reasoning by leveraging previous answers as prompts for subsequent rounds. Extensive experiments across multiple models, including QwQ-32B and DeepSeek-R1, consistently show performance improvements on various benchmarks such as AIME 2024, MATH-500, GPQA-diamond, and LiveCodeBench. For instance, the accuracy of QwQ-32B improved from 80.3% (Round 1) to 82.1% (Round 2) on the AIME 2024 dataset, while DeepSeek-R1 showed a similar increase from 79.7% to 82.0%. These results confirm that Multi-round Thinking is a broadly applicable, straightforward approach to achieving stable enhancements in model performance, underscoring its potential for future developments in test-time scaling techniques. The key prompt: {Original question prompt} The assistant's previous answer is: <answer> {last round answer} </answer>, and please re-answer.
Forward citations
Cited by 7 Pith papers
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Sticker-TTS uses three collaborating models to distill and reuse compact summaries (stickers) of past reasoning attempts, improving math benchmark accuracy at a claimed equivalent inference cost.
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Scaling Up, Speeding Up: A Benchmark of Speculative Decoding for Efficient LLM Test-Time Scaling
N-gram based speculative decoding methods, especially SAM and hybrid SAM[EAGLE-3], achieve strong speedups in LLM test-time scaling by exploiting repetitive reasoning patterns.
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Chained fresh-root model calls with plain-text artifacts improve reported long-context reasoning accuracy over a single-call baseline, but the evidence lacks error bars and compute-matched comparison.
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EDGE-GRPO: Entropy-Driven GRPO with Guided Error Correction for Advantage Diversity
EDGE-GRPO reduces advantage collapse in GRPO by injecting reference solutions into response groups and scaling advantages by policy entropy, achieving competitive math reasoning with only 1K training samples.
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Adaptive Termination for Multi-round Parallel Reasoning: An Universal Semantic Entropy-Guided Framework
A semantic entropy-guided stopping rule for multi-round parallel LLM reasoning improves accuracy while reducing inference steps on five benchmarks.
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Revisiting Test-Time Scaling: A Survey and a Diversity-Aware Method for Efficient Reasoning
ADAPT, a diversity-aware prefix fine-tuning method, improves best-of-N sampling efficiency for a 1.5B reasoning model, reaching 80% accuracy at N=32 versus N=256 for the baseline.
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MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs
A multi-round modular thinking RL fine-tuning method improves a 3B model's pass@1 on MATH500 and AIME2024 over vanilla GRPO in one run, with sample-efficiency claims based on 15% of training questions.
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